Detection and Recognition of Traffic Sign using FCM with SVM

This paper mainly focuses on Traffic Sign and board Detection systems that have been placed on roads and highway. This system aims to deal with real-time traffic sign and traffic board recognition, i.e. localizing what type of traffic sign and traffic board are appears in which area of an input image at a fast processing time. Our detection module is based on proposed extraction and classification of traffic signs built upon a color probability model using HAAR feature Extraction and color Histogram of Orientated Gradients (HOG).HOG technique is used to convert original image into gray color then applies RGB for foreground. Then the Support Vector Machine (SVM) fetches the object from the above result and compares with database. At the same time Fuzzy Cmeans cluster (FCM) technique get the same output from above result and then  to compare with the database images. By using this method, accuracy of identifying the signs could be improved. Also the dynamic updating of new signals can be done. The goal of this work is to provide optimized prediction on the given sign

Usage of convolutional neural network ensemble for traffic sign recognition

Предлагается для распознавания дорожных знаков использовать ансамбль сверточных нейронных сетей, который является модификацией робастного метода распознавания на основе нейронных сетей глубокого обучения. Данный ансамбль повышает скорость работы робастного метода распознавания, а также позволяет увеличить быстродействие с сохранением высокой точности распознавания за счет удаления из набора данных значений, которые не представляют полезной нагрузки

An optimization on pictogram identification for the road-sign recognition task using svms

Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3?5% with a reduction in the number of support vectors of 50?70%

Інтелектуальна система розпізнавання елементів дорожнього руху

У роботі розглянуто проблему розпізнавання елементів дорожнього руху у відео потоці, проведено аналіз наявних проблем та складнощів в існуючих методах розпізнавання елементів та порівняння їхніх характеристик точності та швидкодії, переваг та недоліків. Розроблено інтелектуальну систему розпізнавання елементів дорожнього руху за допомогою алгоритмів машинного навчання та нейронних мереж. Система може бути використана у відео реєстраторах та у системах пасивної безпеки автомобіля. Загалом в роботі розкрито питання призначення та доцільність використання нейронної мережі та представлена програмна реалізація системи за допомогою мови програмування C# та бібліотеки Accord.NET, основними вимогами якої є: прийнятна точність розпізнавання, можливість використання відео потоку в якості вхідних даних, знайдені елементи повинні бути інтуїтивно виділені серед інших елементів та простота в налагоджені. Окремо було приділено увагу локальним результатам експериментів, що дають уявлення про характеристики запропонованої системи. Ключові слова: інтелектуальна система, нейронна мережа, машинне навчання, алгоритм, комп’ютерний зір, дорожній рух. Розмір пояснювальної записки – 81 аркушів, містить 23 ілюстрацій, 28 таблиць, 6 додатків.Examines the problem of recognition of traffic elements in the video stream, analyzes the existing problems and complexities in the existing methods of recognition of the elements and compares their characteristics of accuracy and speed, advantages and disadvantages. An intelligent system for recognizing traffic elements is using machine learning algorithms and neural networks. The system can be used in video recorders and passive vehicle security systems. In general, the paper addresses the purpose and feasibility of using a neural network and presents the software implementation of the system using the C# programming language and the Accord.NET library. The main requirements of which are: acceptable recognition accuracy, the ability to use video stream as input, found elements should be intuitive highlighted among other elements and simplicity in configuring. Special attention was paid to the local results of the experiments, which give an idea of the characteristics of the proposed system. Explanatory note size – 81 pages, contains 23 illustrations, 28 tables, 6 applications

Assessment of Driver\u27s Attention to Traffic Signs through Analysis of Gaze and Driving Sequences

A driver’s behavior is one of the most significant factors in Advance Driver Assistance Systems. One area that has received little study is just how observant drivers are in seeing and recognizing traffic signs. In this contribution, we present a system considering the location where a driver is looking (points of gaze) as a factor to determine that whether the driver has seen a sign. Our system detects and classifies traffic signs inside the driver’s attentional visual field to identify whether the driver has seen the traffic signs or not. Based on the results obtained from this stage which provides quantitative information, our system is able to determine how observant of traffic signs that drivers are. We take advantage of the combination of Maximally Stable Extremal Regions algorithm and Color information in addition to a binary linear Support Vector Machine classifier and Histogram of Oriented Gradients as features detector for detection. In classification stage, we use a multi class Support Vector Machine for classifier also Histogram of Oriented Gradients for features. In addition to the detection and recognition of traffic signs, our system is capable of determining if the sign is inside the attentional visual field of the drivers. It means the driver has kept his gaze on traffic signs and sees the sign, while if the sign is not inside this area, the driver did not look at the sign and sign has been missed

Kontextsensitive Erkennung und Interpretation fahrrelevanter statischer Verkehrselemente

In dieser Arbeit werden Methoden und Verfahren zur Umwelterkennung und Situationsinterpretation entwickelt, mit denen statische Verkehrselemente (Verkehrszeichen und Ampeln) erkannt und im Kontext der Verkehrssituation interpretiert werden. Die Praxistauglichkeit der entwickelten Methoden und Verfahren wird durch umfangreiche Experimente demonstriert, bei denen auf die Verwendung realer Daten, kostengünstiger Sensorik und Echtzeitverarbeitung Wert gelegt wird